Interpretive Summary: With increased use of high resolution aerial and satellite imagery, object-based image analysis (OBIA) has become more commonplace in recent years due to its ability to extract meaningful image objects by segmentation, and to bridge remote sensing and GIS. The ability to incorporate elements used traditionally in aerial photo interpretation (color, size, shape, texture, pattern, and contextual information), is one of the strengths of OBIA. However, the availability of hundreds of spectral, spatial, and contextual features can also make the determination of optimal features a time consuming or subjective process. We tested three feature selection techniques, Jeffreys-Matusita distance (JM), classification tree analysis (CTA), and feature space optimization (FSO) for object-based vegetation classifications with sub-decimeter digital aerial imagery in arid rangelands of the southwestern U.S. We assessed strengths, weaknesses, and best uses for each method using the criteria of ease of use, ability to rank and/or reduce input features, and classification accuracies. While JM resulted in the highest overall classification accuracies, CTA offered ease of use and ability to rank and reduce features. FSO resulted in the lowest accuracies and lacked a transparent approach. While all methods offered an objective approach for determining suitable features for classifications of sub-decimeter resolution aerial imagery, we concluded that CTA was best suited for this particular application. The described methods offer an objective and easy to implement approach for determining the optimum spectral, spatial and contextual features for object-based image analysis of very high resolution airborne imagery.

Technical Abstract:
The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature election methods have been used in conjunction with OBIA, a robust comparison of the utility and efficiency of approaches would facilitate broader and more effective implementation. In this study, we evaluated three feature selection methods, 1) Jeffreys-Matusita distance (JM), 2) classification tree analysis (CTA), and 3) feature space optimization (FSO) for object-based vegetation classifications with sub-decimeter digital aerial imagery in arid rangelands of the southwestern U.S. We assessed strengths, weaknesses, and best uses for each method using the criteria of ease of use, ability to rank and/or reduce input features, and classification accuracies. For the five sites tested, JM resulted in the highest overall classification accuracies for three sites, while CTA yielded highest accuracies for two sites. FSO resulted in the lowest accuracies. CTA offered ease of use and ability to rank and reduce features, while JM had the advantage of assessing class separation distances. FSO allowed for determining features relatively quickly, because it operates within the OBIA software used in this analysis (eCognition). However, the feature ranking in FSO is not transparent and accuracies were relatively low. While all methods offered an objective approach for determining suitable features for classifications of sub-decimeter resolution aerial imagery, we concluded that CTA was best suited for this particular application. We explore the limitations, assumptions, and appropriate uses for this and other datasets.